Big Deal For Education If the Bell Curve is Dead

Big Deal For Education If the Bell Curve is Dead

A new study may disprove the bell curve. For educators, both those who teach and assess students and those who care about the ability of our intuitions to innovate, these findings may have epic implications. Today I will focus on the potential impact on student learning; tomorrow I will try to catch up with thoughts on how this work impacts us at an institutional level, and in particular, on our innovation strategies.

As reported today on NPR Morning Edition by Shankar Vedantam, researchers Ernest O’Boyle Jr. of the College of Business and Economics at Longwood University and Herman Aguinis at the Kelly School of Business at Indiana University report on a study of the performance of more than 630,000 people across four broad areas of performance, including academics, athletics, politics, and entertainment. The core finding, reports Vedantam, is that “a small minority of superstar performers contribute a disproportionate amount of the output”.

Aguinis stated that their research shows that a group of superstars, in most cases, accounted for much of the success of groups as a whole. Their research suggests that the vast majority of others performed below the mathematical average. (For others like me who do not focus on statistics, the translation of this is: the bell curve is not accurate; we have been fitting the outcomes to the expectation, and not the other way around.) Aguinis believes the traditional reliance on bell curve results may describe performance constrained by some external factor, and he offers the example of an assembly line moving at a set speed. “If you have a superstar performer working at your factory…that person could not do (a) better job than the assembly line would allow”, Aguinis says.

In their paper “The Best and the Rest: Revisiting the Norm of Normality of Individual Performance”, O’Boyle and Aguinis describe their research and propose that performance data are more accurately reflected by a Paretian, or power law, distribution that allows for a greater proportion of extreme events. Rather than discarding extreme performances as outliers, and therefore dismissing them from calculated results of the group, the Paretian model includes all data, and it does not look at all like our familiar bell curve.

This research resonates particularly with those of us who believe the industrial model of education is foundationally flawed. Since we provide K-12 education through a relatively static quantum of subject material and time, superstars are constrained in their performance at the upper limits. Students cannot generally receive a higher grade than A+; course work is limited at the high end by the most rigorous course material that a school offers; opportunities to perform outside of the basic model are limited. On the most widely used tests of performance like the SAT, the top score is 800 regardless of whether the test taker could have scored better.

Not all, but much, of our academic course work is geared to the preconception that the group of students will generally perform according to a Gaussian distribution. What if that is wrong? What if we questioned that assumption, and removed all constraints on the upper end of performance? What if our assessment protocols had a way to quantify superstar contributions or performance relative to all other performance? This research suggests that the overall assessment profiles would look very different.

Some will leap to the incorrect conclusion that this would result in more attention being paid to the superstar students to the detriment of all others. In fact, given some focus, the opposite should be the likely outcome. Once we realize the true shape of the performance curve we can focus resources on (according to this research) the larger group that is actually performing below our past expectations and find ways to increase their contributions and performance.

This research should provide a fascinating opportunity for data mining for educators who understand statistics. Later today or tomorrow I will put down some thoughts on how the research impacts institutional structures. This is just the start of a lot of important conversations!